AI Health Index: Drug Discovery AI changes https://aihealthindex.io/categories/drug-discovery Material product changes at Drug Discovery AI vendors, tracked by AI Health Index. Every entry is dated and cited to a public source. en-us Free to reuse with a visible link to aihealthindex.io. Terms: https://aihealthindex.io/use-this-data Thu, 24 Sep 2026 12:00:00 GMT 720 https://aihealthindex.io/icon-192.png AI Health Index: Drug Discovery AI changes https://aihealthindex.io/categories/drug-discovery Edison Scientific: Kosmos can now read licensed full text research from specialist journals across Springer Nature's Nature portfolio when it generates… https://aihealthindex.io/changelog/edison-scientific aihealthindex.io:change:6ab90c10ee3c2977980e6542 Thu, 24 Sep 2026 12:00:00 GMT Product / capability Drug Discovery AI Clinical Trials AI Kosmos can now read licensed full text research from specialist journals across Springer Nature's Nature portfolio when it generates hypotheses or evaluates targets, under a subscription agreement Edison announced on 24 September. Kosmos links each surfaced article to its Version of Record, so outputs reflect corrected versions including retractions and editors' notes. Edison states the Nature portfolio is accessible in Kosmos as of the announcement date and calls it the first of several content partnerships.

Why it matters: R&D teams evaluating an AI scientist need to know which licensed literature grounds its conclusions, and retraction aware Version of Record linking reduces the risk of hypotheses built on withdrawn findings.

Impact: Medium. Verification: Verified. Type: Product / capability.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Insilico Medicine: Insilico announced that Model Context Protocol servers are now available across its Pharma.AI software, so outside AI agents can connect… https://aihealthindex.io/changelog/insilico-medicine aihealthindex.io:change:6ab85e9bd92e5616ac6dc816 Thu, 24 Sep 2026 12:00:00 GMT Product / capability Drug Discovery AI Clinical Trials AI Insilico announced that Model Context Protocol servers are now available across its Pharma.AI software, so outside AI agents can connect directly to its biology, chemistry and biologics engines, including Generative Biologics, PandaOmics and Chemistry42, and run multistep discovery workflows. The announcement previews a 30 September webinar that will present new PandaOmics Agent skills and other platform updates.

Why it matters: MCP access lets a customer's own agents drive Insilico's engines, which turns Pharma.AI from a set of applications into a component of the customer's stack. Teams should ask how data sent through those servers is logged and whether it is used to train Insilico's models.

Impact: Medium. Verification: Verified. Type: Product / capability.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Deciphex launched CipherX, a pathology AI engine that pairs foundation models with a semantic layer translating their representations into… https://aihealthindex.io/changelog/deciphex aihealthindex.io:change:6ab85e9bd92e5616ac6dc811 Tue, 22 Sep 2026 12:00:00 GMT Model / architecture Digital Pathology AI Drug Discovery AI Clinical Trials AI Deciphex launched CipherX, a pathology AI engine that pairs foundation models with a semantic layer translating their representations into named, pathologist validated tissue signatures. Deciphex states CipherX is in production in its Diagnexia clinical diagnostic service and its Patholytix research business, and reports negative predictive values of 99.85 percent for adenocarcinoma and 98.76 percent for melanoma in production, with every Diagnexia case signed out by a pathologist.

Why it matters: Labs using Diagnexia get CipherX inside the service rather than as a separate purchase. The predictive values are vendor reported, so ask for the case mix and prevalence behind them and which signatures are used in clinical sign out today rather than research only.

Impact: High. Verification: Verified. Type: Model / architecture.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Iambic Therapeutics: Iambic released Enchant v3, the third generation of the multimodal transformer behind its drug discovery platform. https://aihealthindex.io/changelog/iambic-therapeutics aihealthindex.io:change:6ab85e9bd92e5616ac6dc80c Mon, 21 Sep 2026 12:00:00 GMT Model / architecture Drug Discovery AI Iambic released Enchant v3, the third generation of the multimodal transformer behind its drug discovery platform. Iambic states the model has 41 billion parameters, was pretrained on 4.5 trillion tokens, and covers more than 6,000 molecular properties across 16 biomedical data modalities using a mixture of experts architecture. Iambic says it will be used on internal and partner programs.

Why it matters: Enchant v3 is the model partners get when they work with Iambic, so its prediction accuracy is the thing being bought. Iambic says scaling has held across three generations, including on endpoints with sparse data. Ask for the benchmark results in the Enchant v3 report and how they hold on targets with little training data.

Impact: High. Verification: Verified. Type: Model / architecture.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Aignostics released PathoSearch in early access, a visual search engine for pathology: from a screenshot of a region of interest on an H… https://aihealthindex.io/changelog/aignostics aihealthindex.io:change:6aa5b27795f1c7683c18cf6f Sat, 12 Sep 2026 12:00:00 GMT Product / capability Digital Pathology AI Drug Discovery AI Clinical Trials AI Aignostics released PathoSearch in early access, a visual search engine for pathology: from a screenshot of a region of interest on an H and E slide it retrieves morphologically similar, diagnosed reference cases so a pathologist can build a differential in minutes. Search runs on embeddings from Atlas 2, Aignostics' pathology foundation model co developed with Mayo Clinic, against a curated multi center reference set of more than 310,000 whole slide images from over 35,000 cases spanning more than 300 diagnostic entities across 27 organs, covering thoracic, digestive, soft tissue and bone, female genital, and urinary and male genital cancers at launch. The first integration is live inside Techcyte's Fusion AP slide viewer, where a selected region is passed to PathoSearch without export or upload. PathoSearch is free during early access, by waitlist, and is labeled research use only, not for diagnostic procedures.

Why it matters: Rare and complex cases are where a pathologist reaches for a textbook or a paid consult, and image similarity search against a diagnosed reference set is a plausible replacement for the first of those. The research use only label is the operative constraint: this cannot be part of a signed out diagnosis today, and a lab evaluating it should treat the Fusion AP integration as the way to try it inside the workflow rather than as a clinical deployment.

Impact: Medium. Verification: Partially Verified. Type: Product / capability.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Insilico Medicine: Insilico released a set of small language models trained as scientific specialists for chemistry and biology through its MMAI Gym for… https://aihealthindex.io/changelog/insilico-medicine aihealthindex.io:change:6a9c312c04c538181acf1b3a Tue, 01 Sep 2026 12:00:00 GMT Model / architecture Drug Discovery AI Clinical Trials AI Insilico released a set of small language models trained as scientific specialists for chemistry and biology through its MMAI Gym for Science framework. The set covers chemical synthesis, ADMET prediction and potency prediction across GPCR and kinase panels, and includes a single step retrosynthesis model built on Liquid AI's 2.6 billion parameter architecture.

Why it matters: Naming the base architecture and the parameter count is unusual in this category and is the reason this is graded on model transparency rather than capability alone. For discovery teams the practical read is that these are narrow specialists rather than a general assistant, which is the right shape for ADMET and potency work where a confident wrong answer is expensive.

Impact: High. Verification: Verified. Type: Model / architecture.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Insilico Medicine convened the Open Consortium for Benchmark Quality in AI Driven Drug Discovery, known as O3DC, and published a live… https://aihealthindex.io/changelog/insilico-medicine aihealthindex.io:change:6a92e5ef75bbcbbe469c6fea Mon, 24 Aug 2026 12:00:00 GMT Safety / governance Drug Discovery AI Clinical Trials AI Insilico Medicine convened the Open Consortium for Benchmark Quality in AI Driven Drug Discovery, known as O3DC, and published a live catalog of the benchmarks the field uses to claim performance. The catalog tracks each benchmark's metrics, maintainers and repository activity, and unusually it documents the known caveats, biases and limitations of each one rather than presenting them as neutral yardsticks. The stated premise is that a benchmark with an undisclosed bias produces model comparisons that look rigorous and are not.

Why it matters: Pharmaceutical evaluators comparing AI drug discovery platforms are almost always comparing benchmark scores, and this is the first centralized attempt to say which of those benchmarks can carry the weight. It is worth noting that the vendor convening a benchmark integrity consortium is also a vendor whose own models are scored on those benchmarks, so the catalog is useful and is not disinterested. Read it as a map of where the measurement problems are rather than as an independent audit.

Impact: Medium. Verification: Verified. Type: Safety / governance.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Paige launched an AI tool that screens 505 genes directly from H and E stained pathology slides, without requiring next generation… https://aihealthindex.io/changelog/paige aihealthindex.io:change:6a92e5ef75bbcbbe469c6fe7 Sun, 23 Aug 2026 12:00:00 GMT Product / capability Digital Pathology AI Diagnostics & Genomics Drug Discovery AI Paige launched an AI tool that screens 505 genes directly from H and E stained pathology slides, without requiring next generation sequencing as a first step. The model predicts a comprehensive genomic profile from routine morphology alone and flags potential biomarkers for follow up. Rather than replacing sequencing, it is positioned as a triage layer that decides which cases warrant the full molecular workup. Paige is applying the same foundation model approach it built for cancer detection to molecular prediction.

Why it matters: Pathology labs and oncology practices can screen for hundreds of mutations off slides they already produce, compressing the time between biopsy and a targeted therapy decision. The economic argument is sequencing avoidance: labs run the expensive molecular test on the subset the model flags rather than on everything. Buyers should ask which of the 505 genes have been validated against sequencing ground truth and at what sensitivity, because a screening tool that misses a targetable mutation carries a different risk profile than one that over refers.

Impact: High. Verification: Verified. Type: Product / capability.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Owkin received European regulatory approval for two first-in-class AI diagnostic solutions designed for breast cancer and colorectal cancer. https://aihealthindex.io/changelog/owkin aihealthindex.io:change:6a89d007fbe75a71b1da726b Tue, 18 Aug 2026 12:00:00 GMT Regulatory / FDA Drug Discovery AI Clinical Trials AI Owkin received European regulatory approval for two first-in-class AI diagnostic solutions designed for breast cancer and colorectal cancer. The tools leverage multimodal patient data to assist in biomarker screening and outcome prediction.

Why it matters: European healthcare providers and laboratories can now clinically deploy these validated AI diagnostic tools. This provides pathologists and oncologists with approved decision support for breast and colorectal cancer diagnostics.

Impact: High. Verification: Verified. Type: Regulatory / FDA.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Insilico Medicine published a collaborative study in npj Precision Oncology demonstrating the use of its PandaOmics AI platform to… https://aihealthindex.io/changelog/insilico-medicine aihealthindex.io:change:6a7739f386781bb15a7c68cc Tue, 04 Aug 2026 12:00:00 GMT Clinical evidence Drug Discovery AI Clinical Trials AI Insilico Medicine published a collaborative study in npj Precision Oncology demonstrating the use of its PandaOmics AI platform to identify therapeutic targets for inverted papilloma-associated sinonasal squamous cell carcinoma. The platform integrated transcriptomic data with pathway biology and protein interaction networks to map the molecular cascade of the disease.

Why it matters: This peer-reviewed publication provides concrete validation of the platform's ability to discover actionable targets in rare and poorly understood cancers. Buyers evaluating the software can use this evidence to assess its utility in complex multi-omic analysis and translational research.

Impact: Medium. Verification: Verified. Type: Clinical evidence.

Evidence: Vendor announcement

Source: AI Health Index change log. Free to reuse with a link back. Terms.

]]> Chai Discovery deployed Chai-3, a next generation antibody design model that the company describes as roughly doubling the success rate of… https://aihealthindex.io/changelog/chai-discovery aihealthindex.io:change:6a5d3fdd0b03892a4bc1369b Sun, 12 Jul 2026 12:00:00 GMT Model / architecture Drug Discovery AI Chai Discovery deployed Chai-3, a next generation antibody design model that the company describes as roughly doubling the success rate of Chai-2 and binding more tightly to harder targets. Date recorded here is the date of the trade coverage confirming deployment; the company has not published a precise release date for Chai-3, and this record does not assert one.

Why it matters: For an R&D buyer the question is whether a model generation actually changes the design cycle or only the benchmark. The reported doubling is a vendor claim measured against the company's own prior model, not an independent comparison against competing methods, and no candidate designed by these models has published clinical results. What is verifiable is that named pharmaceutical organizations have licensed the platform into production R&D, including at least one custom model trained on a customer's proprietary data. That structure is the thing to scrutinize in procurement: a custom model trained on internal discovery data creates a different dependency, and different exit economics, than licensing a general model.

Impact: High. Verification: Partially Verified. Type: Model / architecture.

Evidence: Trade press

Source: AI Health Index change log. Free to reuse with a link back. Terms.

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